1 Sample Metadata

treatment age_group patient_id sample Sequence
0 2 3 160008699_3_0_S5 1
1 2 3 160008699_3_8_S6 2
0 2 4 290001824_4_0_S7 3
1 2 4 290001824_4_8_S8 4
0 1 17 330001842_17_0_S31 5
1 1 17 330001842_17_8_S32 6
0 0 5 470009458_5_0_S9 7
1 0 5 470009458_5_4_S10 8
0 1 13 660009823_13_0_S25 9
1 1 13 660009823_13_8_S26 10
0 0 11 770004766_11_0_S21 11
1 0 11 770004766_11_8_S22 12
0 1 2 830001304_2_0_S3 13
1 1 2 830001304_2_4_S4 14
0 2 12 830002078_12_0_S23 15
1 2 12 830002078_12_8_S24 16
0 2 9 880001252_9_0_S17 17
1 2 9 880001252_9_8_S18 18
0 0 8 940004357_8_0_S15 19
1 0 8 940004357_8_8_S16 20
0 1 7 970002731_7_0_S13 21
1 1 7 970002731_7_4_S14 22
0 0 10 980007758_10_0_S19 23
1 0 10 980007758_10_8_S20 24

2 WGCNA result

Soft threshold = 16

soft threshold = 16

.

Modules = 29

29 modules in total

.

Positive modules Spearman correlation (p-value)
lightgreen (152 genes) 0.14 (0.1)
Negative modules Spearman correlation (p-value)
darkred (63 genes) -0.12 (0.2)
midnightblue (303 genes) -0.1 (0.2)

3 Run LASSO on treatment-positive modules (Module lightgreen: 152 genes)

  • Alpha = 1

  • Nested cross validation

    • outer loop method: leave-one-out
    • inner loop method: leave-one-out
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Tuned lambda value:
##  0.04302683
## 
## Call:  cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet,      1), family = ..1, penalty.factor = ..3) 
## 
## Measure: Binomial Deviance 
## 
##      Lambda Index Measure     SE Nonzero
## min 0.04303    41   1.042 0.2130      10
## 1se 0.08645    26   1.244 0.1541       7
## Non-zero Coefficients:
##  ENSG00000152894 ENSG00000084072 ENSG00000101057 ENSG00000173898 ENSG00000026559 ENSG00000058091 ENSG00000134532 ENSG00000112232 ENSG00000166833 ENSG00000168502

3.1 List of genes with non-zero coefficients (10 Genes)

ensembl_gene_id external_gene_name
ENSG00000026559 KCNG1
ENSG00000058091 CDK14
ENSG00000084072 PPIE
ENSG00000101057 MYBL2
ENSG00000112232 KHDRBS2
ENSG00000134532 SOX5
ENSG00000152894 PTPRK
ENSG00000166833 NAV2
ENSG00000168502 MTCL1
ENSG00000173898 SPTBN2

3.2 AUC

3.3 Accuracy

##          Reference
## Predicted  0  1
##         0 10  3
##         1  2  9
##               AUC          Accuracy Balanced accuracy 
##         0.7708333         0.7916667         0.7916667

3.4 TPM (0: pre vs. 1: post)

sample KCNG1 CDK14 PPIE MYBL2 KHDRBS2 SOX5 PTPRK NAV2 MTCL1 SPTBN2 treatment patient_id
160008699_3_0_S5 0.65 6.49 21.15 1.22 0.52 0.04 0.74 0.71 0.30 0.65 0 3
160008699_3_8_S6 0.94 5.21 16.44 1.04 0.46 0.17 1.37 0.63 0.44 0.05 1 3
290001824_4_0_S7 0.89 7.49 17.39 0.89 0.31 1.80 0.58 0.47 0.48 0.36 0 4
290001824_4_8_S8 1.28 8.77 17.77 2.54 0.48 0.37 2.68 0.87 0.34 0.19 1 4
330001842_17_0_S31 0.51 3.21 12.59 1.86 0.38 0.63 1.01 0.35 0.41 0.24 0 17
330001842_17_8_S32 0.93 5.54 10.86 1.99 0.41 0.36 2.35 0.23 0.43 0.09 1 17
470009458_5_0_S9 0.55 4.61 11.87 1.61 0.25 1.62 1.07 0.88 0.32 0.27 0 5
470009458_5_4_S10 1.03 5.35 12.52 1.90 0.27 2.58 3.05 0.27 0.27 0.21 1 5
660009823_13_0_S25 0.22 3.87 12.76 2.09 0.30 0.89 0.91 0.73 0.23 0.20 0 13
660009823_13_8_S26 0.13 8.58 5.29 0.76 0.20 0.19 1.00 0.27 0.31 0.28 1 13
770004766_11_0_S21 0.13 3.58 7.84 0.80 0.10 0.11 0.44 0.06 0.28 0.01 0 11
770004766_11_8_S22 0.26 6.32 9.87 1.07 0.15 0.34 0.76 0.05 0.14 0.10 1 11
830001304_2_0_S3 0.14 7.03 18.19 1.46 0.37 2.10 1.95 1.10 0.78 0.41 0 2
830001304_2_4_S4 0.37 10.43 14.59 0.99 0.34 0.97 2.48 0.65 0.32 0.13 1 2
830002078_12_0_S23 0.32 2.71 10.75 1.09 0.13 0.43 0.60 0.51 0.45 0.70 0 12
830002078_12_8_S24 0.30 2.10 7.85 0.16 0.71 0.11 0.59 0.26 0.20 0.03 1 12
880001252_9_0_S17 0.29 7.50 18.17 0.95 0.48 1.28 1.38 0.29 0.35 1.61 0 9
880001252_9_8_S18 0.48 5.82 11.21 0.77 0.36 0.53 1.72 0.07 0.26 0.19 1 9
940004357_8_0_S15 0.09 3.65 13.71 0.77 0.16 0.42 0.93 1.05 0.16 0.07 0 8
940004357_8_8_S16 0.60 6.42 8.37 0.85 0.40 0.71 1.78 1.44 0.37 0.36 1 8
970002731_7_0_S13 0.22 5.13 10.27 3.31 0.19 0.30 0.51 0.46 0.28 0.11 0 7
970002731_7_4_S14 1.09 6.11 9.80 0.28 0.75 0.28 1.39 0.25 0.59 0.69 1 7
980007758_10_0_S19 1.01 5.42 16.09 4.64 0.39 0.58 1.50 0.31 0.32 0.44 0 10
980007758_10_8_S20 1.19 6.72 12.64 4.22 0.58 0.70 2.82 0.28 0.44 0.06 1 10

3.5 Heatmap (Pre-treatment vs Post-treatment)

3.6 Heatmap for Log-FoldChange for each patient

## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.

4 Run LASSO on treatment-negative modules (Module darkred: 63 genes)

  • Alpha = 1

  • Nested cross validation

    • outer loop method: leave-one-out
    • inner loop method: k-fold; k = 4
    • Didn’t find any non-zero coeffients when using leave-one-out for inner loop
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Tuned lambda value:
##  0.1464232
## 
## Call:  cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet,      1), family = ..1, penalty.factor = ..3) 
## 
## Measure: Binomial Deviance 
## 
##     Lambda Index Measure      SE Nonzero
## min 0.1464    10   1.515 0.12399       4
## 1se 0.2225     1   1.524 0.07232       0
## Non-zero Coefficients:
##  ENSG00000279982 ENSG00000271550 ENSG00000272502 ENSG00000169519

4.1 AUC

4.2 Accuracy

##    
##      0  1
##   0 12  0
##   1  0 12
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   1.000000e+00   1.000000e+00   8.575264e-01   1.000000e+00   5.000000e-01 
## AccuracyPValue  McnemarPValue 
##   5.960464e-08            NaN

4.3 List of genes with non-zero coefficients (4 genes)

ensembl_gene_id external_gene_name
ENSG00000169519 METTL15
ENSG00000271550 BNIP3P11
ENSG00000272502 ENSG00000272502
ENSG00000279982 ENSG00000279982

4.4 TPM (0: pre vs. 1: post)

sample METTL15 BNIP3P11 ENSG00000272502 ENSG00000279982 treatment patient_id
160008699_3_0_S5 5.35 2.31 0.69 0.17 0 3
160008699_3_8_S6 2.49 0.57 1.36 0.13 1 3
290001824_4_0_S7 3.64 2.81 2.28 0.25 0 4
290001824_4_8_S8 3.16 2.88 1.16 0.29 1 4
330001842_17_0_S31 1.91 1.74 0.76 0.17 0 17
330001842_17_8_S32 2.29 0.88 0.26 0.16 1 17
470009458_5_0_S9 1.98 0.84 1.03 0.11 0 5
470009458_5_4_S10 1.63 0.55 0.45 0.12 1 5
660009823_13_0_S25 3.71 3.26 0.74 0.58 0 13
660009823_13_8_S26 0.76 0.38 0.38 0.06 1 13
770004766_11_0_S21 1.05 0.54 0.37 0.06 0 11
770004766_11_8_S22 1.83 0.91 0.68 0.05 1 11
830001304_2_0_S3 4.07 2.34 1.43 0.33 0 2
830001304_2_4_S4 2.69 1.15 0.58 0.21 1 2
830002078_12_0_S23 2.33 1.56 0.70 0.28 0 12
830002078_12_8_S24 2.01 1.27 0.65 0.12 1 12
880001252_9_0_S17 3.53 3.41 1.53 0.38 0 9
880001252_9_8_S18 2.06 0.89 0.54 0.15 1 9
940004357_8_0_S15 1.91 1.01 0.76 0.18 0 8
940004357_8_8_S16 1.64 0.69 0.45 0.23 1 8
970002731_7_0_S13 2.70 1.10 0.40 0.20 0 7
970002731_7_4_S14 1.61 1.43 0.45 0.10 1 7
980007758_10_0_S19 2.80 2.17 1.29 0.28 0 10
980007758_10_8_S20 2.05 2.32 0.75 0.14 1 10

4.5 Heatmap (Pre-treatment vs Post-treatment)

4.6 Heatmap for Log-FoldChange for each patient

## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.

5 Run LASSO on treatment-negative module (Module midnightblue: 303 genes)

  • Alpha = 1

  • Nested cross validation

    • outer loop method: leave-one-out
    • inner loop method: k-fold; k = 4
    • Didn’t find any non-zero coeffients when using leave-one-out for inner loop
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground

## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Tuned lambda value:
##  0.2217997
## 
## Call:  cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet,      1), family = ..1, penalty.factor = ..3) 
## 
## Measure: Binomial Deviance 
## 
##     Lambda Index Measure      SE Nonzero
## min 0.2218     2   1.509 0.09580       1
## 1se 0.2324     1   1.509 0.09087       0
## Non-zero Coefficients:
##  ENSG00000186073

5.1 AUC

5.2 Accuracy

##    
##      0  1
##   0 12  0
##   1  0 12
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   1.000000e+00   1.000000e+00   8.575264e-01   1.000000e+00   5.000000e-01 
## AccuracyPValue  McnemarPValue 
##   5.960464e-08            NaN

5.3 List of genes with non-zero coefficients (1 gene)

ensembl_gene_id external_gene_name
ENSG00000186073 CDIN1

5.4 TPM (0: pre vs. 1: post)

sample CDIN1 treatment patient_id
160008699_3_0_S5 1.14 0 3
160008699_3_8_S6 1.35 1 3
290001824_4_0_S7 2.48 0 4
290001824_4_8_S8 1.72 1 4
330001842_17_0_S31 1.36 0 17
330001842_17_8_S32 1.58 1 17
470009458_5_0_S9 1.04 0 5
470009458_5_4_S10 0.96 1 5
660009823_13_0_S25 1.94 0 13
660009823_13_8_S26 0.58 1 13
770004766_11_0_S21 0.74 0 11
770004766_11_8_S22 0.52 1 11
830001304_2_0_S3 2.13 0 2
830001304_2_4_S4 1.61 1 2
830002078_12_0_S23 1.24 0 12
830002078_12_8_S24 0.81 1 12
880001252_9_0_S17 1.95 0 9
880001252_9_8_S18 0.77 1 9
940004357_8_0_S15 1.05 0 8
940004357_8_8_S16 0.72 1 8
970002731_7_0_S13 1.43 0 7
970002731_7_4_S14 0.83 1 7
980007758_10_0_S19 1.74 0 10
980007758_10_8_S20 1.15 1 10

5.5 Heatmap (Pre-treatment vs Post-treatment)

5.6 Heatmap for Log-FoldChange for each patient

## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.

6 Final model

6.1 Final Lasso nested cv (Total 15 genes)

## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
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## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
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## Tuned lambda value:
##  0.02243423
## 
## Call:  cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet,      1), family = ..1, penalty.factor = ..3) 
## 
## Measure: Binomial Deviance 
## 
##      Lambda Index Measure     SE Nonzero
## min 0.02243    28  0.9762 0.3644      10
## 1se 0.19064     5  1.2901 0.0605       2
## Non-zero Coefficients:
##  ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898 ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550 ENSG00000134532

6.1.1 AUC

6.1.2 Accuracy of nested cv

##          Reference
## Predicted  0  1
##         0 10  3
##         1  2  9
##               AUC          Accuracy Balanced accuracy 
##         0.8611111         0.7916667         0.7916667

6.2 Final model (10 genes)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
## Call:
## glm(formula = formula_str, family = binomial, data = data.frame(final_model_matrix))
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(>|z|)
## (Intercept)     -6.098e+00  1.006e+05       0        1
## ENSG00000152894  1.064e+01  2.610e+05       0        1
## ENSG00000186073 -2.001e+01  1.661e+05       0        1
## ENSG00000112232  2.255e+01  1.773e+05       0        1
## ENSG00000173898 -3.164e+01  2.157e+05       0        1
## ENSG00000058091  2.196e+01  2.018e+05       0        1
## ENSG00000084072 -1.369e+00  1.491e+05       0        1
## ENSG00000166833 -3.067e+00  1.208e+05       0        1
## ENSG00000101057 -7.221e-01  2.505e+05       0        1
## ENSG00000271550  8.555e-01  2.179e+05       0        1
## ENSG00000134532  2.729e+00  1.245e+05       0        1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 3.3271e+01  on 23  degrees of freedom
## Residual deviance: 3.8444e-10  on 13  degrees of freedom
## AIC: 22
## 
## Number of Fisher Scoring iterations: 25

6.2.1 Check correlation between each feature

6.3 Check complete separation (perfect prediction)

## Implementation: ROI | Solver: lpsolve 
## Separation: TRUE 
## Existence of maximum likelihood estimates
##     (Intercept) ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898 
##            -Inf             Inf            -Inf             Inf            -Inf 
## ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550 
##             Inf            -Inf             Inf            -Inf            -Inf 
## ENSG00000134532 
##            -Inf 
## 0: finite value, Inf: infinity, -Inf: -infinity

6.4 Method 1: Bayes

## bayesglm(formula = formula_str, family = binomial(link = "logit"), 
##     data = as.data.frame(final_model_matrix))
##                 coef.est coef.se
## (Intercept)     -0.12     0.82  
## ENSG00000152894  1.90     1.05  
## ENSG00000186073 -0.90     0.92  
## ENSG00000112232  0.98     0.75  
## ENSG00000173898 -0.73     0.85  
## ENSG00000058091  0.72     0.78  
## ENSG00000084072 -0.46     0.81  
## ENSG00000166833 -0.38     0.67  
## ENSG00000101057 -0.49     0.78  
## ENSG00000271550 -0.46     0.84  
## ENSG00000134532 -0.42     0.74  
## ---
## n = 24, k = 11
## residual deviance = 4.4, null deviance = 33.3 (difference = 28.9)
## 
## Call:  bayesglm(formula = formula_str, family = binomial(link = "logit"), 
##     data = as.data.frame(final_model_matrix), method = "detect_separation")
## 
## Coefficients:
##     (Intercept)  ENSG00000152894  ENSG00000186073  ENSG00000112232  
##         -0.1212           1.9042          -0.9044           0.9826  
## ENSG00000173898  ENSG00000058091  ENSG00000084072  ENSG00000166833  
##         -0.7303           0.7199          -0.4559          -0.3832  
## ENSG00000101057  ENSG00000271550  ENSG00000134532  
##         -0.4893          -0.4556          -0.4180  
## 
## Degrees of Freedom: 23 Total (i.e. Null);  13 Residual
## Null Deviance:       33.27 
## Residual Deviance: 4.371     AIC: 26.37

6.4.1 AUC:bayes

6.5 Method 2: Firth’s Bias-Reduced Logistic Regression

Firth’s bias reduction method, equivalent to penalization of the log-likelihood

## logistf(formula = formula_str, data = as.data.frame(final_model_matrix))
## 
## Model fitted by Penalized ML
## Coefficients:
##                        coef  se(coef) lower 0.95 upper 0.95      Chisq
## (Intercept)     -0.12162486 0.4778238  -1.951730   1.132856 0.04654081
## ENSG00000152894  0.80908839 0.9235939  -1.807387   5.566714 0.56450734
## ENSG00000186073 -0.38336802 0.8365246  -3.149846   1.940889 0.15791745
## ENSG00000112232  0.75311207 0.8088889  -1.108937   4.582622 0.68644923
## ENSG00000173898 -0.71874510 0.6776823  -4.860134   1.561141 0.77934305
## ENSG00000058091  0.93825921 0.7412681  -1.345513   4.437082 0.91025159
## ENSG00000084072 -0.15907838 0.7356271  -2.756363   1.884979 0.03434081
## ENSG00000166833 -0.41668886 0.4749537  -2.084143   1.162984 0.56991526
## ENSG00000101057 -0.64074898 0.7742647  -4.848279   1.731083 0.47489122
## ENSG00000271550 -0.19599018 0.9240849  -3.468708   2.428395 0.03374195
## ENSG00000134532 -0.09648449 0.6567023  -3.243360   1.871623 0.01625704
##                         p method
## (Intercept)     0.8291957      2
## ENSG00000152894 0.4524498      2
## ENSG00000186073 0.6910811      2
## ENSG00000112232 0.4073748      2
## ENSG00000173898 0.3773421      2
## ENSG00000058091 0.3400477      2
## ENSG00000084072 0.8529838      2
## ENSG00000166833 0.4502926      2
## ENSG00000101057 0.4907455      2
## ENSG00000271550 0.8542568      2
## ENSG00000134532 0.8985422      2
## 
## Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
## 
## Likelihood ratio test=16.91808 on 10 df, p=0.07619644, n=24
## Wald test = 11.02228 on 10 df, p = 0.3557845
## logistf(formula = formula_str, data = as.data.frame(final_model_matrix), 
##     method = "detect_separation")
## Model fitted by Penalized ML
## Confidence intervals and p-values by Profile Likelihood 
## 
## Coefficients:
##     (Intercept) ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898 
##     -0.12162486      0.80908839     -0.38336802      0.75311207     -0.71874510 
## ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550 
##      0.93825921     -0.15907838     -0.41668886     -0.64074898     -0.19599018 
## ENSG00000134532 
##     -0.09648449 
## 
## Likelihood ratio test=16.91808 on 10 df, p=0.07619644, n=24
## [1] "AUC (test): 1"

## [1] "Accuracy (test): 1"

Check direction of each gene in two models, all the same

7 Final genes list (Total 10 genes)

Modules (size) Module correlation to treatment Genes selected by lasso
lightgreen (152 genes) Positive 8
darkred (63 genes) Negative 1
midnightblue (303 genes) Negative 1
ensembl_gene_id external_gene_name
1 ENSG00000058091 CDK14
2 ENSG00000084072 PPIE
3 ENSG00000101057 MYBL2
4 ENSG00000112232 KHDRBS2
5 ENSG00000134532 SOX5
6 ENSG00000152894 PTPRK
7 ENSG00000166833 NAV2
8 ENSG00000173898 SPTBN2
9 ENSG00000271550 BNIP3P11
10 ENSG00000186073 CDIN1

7.1 Heatmap (Pre-treatment vs Post-treatment)

7.2 Heatmap for Log-FoldChange for each patient

## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.

8 Pathway Analysis ORA

Reactome

.

KEGG

.